Since multiple frames are exploited to find a relaxed short-time representation, it intuitively leaves an error margin for the temporal structure estimation and leads to a more stable temporal representation. More precisely, instead of using individual frame information in a temporal model as in [1]}, our method leverages a pooling scheme before a temporal model to capture the interdependencies of deep features on multiple video frame (Note that [2]} uses the similar assumption as the sum of trajectory features in time and the authors claim that it improves the accuracy). Thus, this decreases the tendency of temporal models to overfit to the training data.
